A social index is a summary of a complex issue (or issues). Generally, social indexes take a large number of variables related to a specific topic or situation and combine them to get one number. It’s often a single number, but can also be a rank (#1 country out of 180) or a category (“high performing”).

Heather Krause

Pros of social indices:

attract public interest

allow comparisons over time

provide a big picture

„reduce vast amounts of information to a manageable size“

Cons:

„disguise a massive amount of inequality in the data“

simplistic interpretations

hide emerging problems of some variables

So, should we use them?

Krause says, „yes“, but …

If we’re using an index to understand a trend or situation, we also need to look at the individual elements that make up that index.

In my early fieldwork in the computerising offices and factories of the late 1970s and 80s, I discovered the duality of information technology: its capacity to automate but also to “informate”, which I use to mean to translate things, processes, behaviours, and so forth into information. This duality set information technology apart from earlier generations of technology: information technology produces new knowledge territories by virtue of its informating capability, always turning the world into information. The result is that these new knowledge territories become the subject of political conflict. The first conflict is over the distribution of knowledge: “Who knows?” The second is about authority: “Who decides who knows?” The third is about power: “Who decides who decides who knows?”

(…)

Surveillance capitalists were the first movers in this new world. They declared their right to know, to decide who knows, and to decide who decides. In this way they have come to dominate what I call “the division of learning in society”, which is now the central organising principle of the 21st-century social order, just as the division of labour was the key organising principle of society in the industrial age.

It is important to note that productivity growth evolves in a two-stage process: the initial invention of new technologies through research and development, subsequently followed by technological diffusion, i.e. the incorporation of these new technologies in the production processes of firms. As a result, even though many important technology advances may have been invented in recent times, they will only exert an effect on output and productivity once firms utilise these technologies in production. Potential productivity gains from technologies that have yet to be widely adopted may be sizable. A central example is the field of artificial intelligence in which future productivity gains may be considerable once AI-related technologies diffuse to the wider economy. (…)

AI may represent — as did the steam engine, the internal combustion engine and personal computers — a general purpose technology, meaning that it is far-reaching, holds the potential for further future improvements and has the capability of spurring other major, complementary innovations over time with the power of drastically boosting productivity. Incorporating AI in production requires substantial changes on the firm-level, including capital stock adjustments, the revision of internal processes and infrastructures, as well as adapting supply and value chains to enable the absorption of these new technologies. Consequently, this initial adjustment related to the incorporation of general purpose technologies in firms‘ production may take time and may initially even be accompanied by a drop in labour productivity before delivering positive productivity gains.

The best defense against subjectivity in science is to expose it. Transparency in data, methods, and process gives the rest of the community opportunity to see the decisions, question them, offer alternatives, and test these alternatives in further research.